Acta Geodaetica et Geophysica最新文献

The present study carries out subsurface exploration of Jamshedpur region using Active Multichannel Analysis of Surface Waves techniques that provide information on the different lithological characteristics. Four different sites (MASW-1 to MASW-4) were chosen in proximity to river basin to obtain a probable shear wave velocity profile. To record the raw wave field traces produced by a 10 kg sledgehammer, a linear array of 24 numbers of 4.5 Hz geophones was employed. The effects of data acquisition parameters, like sampling frequency (500 Hz, 1000 Hz, and 2000 Hz) and offset distance (1 m, 6 m, 8 m, and 20 m), were used to obtain a high-resolution dispersion image. Due to the variables selected as data acquisition parameters, the optimal set of data parameters was found, providing the best resolution of dispersion images for all the selected sites. The results indicate that the best resolution of the dispersion image was produced at an offset distance range of 6–8 m at sampling frequencies range of 500–1000 Hz at 1 m geophone spacing with five stacking, indicating a strong signal to noise ratio in a range of 80–90%. Up to a depth of ~ 3 m, stiff silty clay soil was discovered, and at depths of 30 m or more, medium- to very-dense weathered mica schist was discovered. At sites MASW-1 and MASW-3, respectively, slag fillings were found in the top layer at depths of 1.2 and 2.3 m. Greater depths of hard rock layers have also been found at site MASW-3. Locations along the river generally fall into National Earthquake Hazards Reduction Program (NEHRP) categories C or D.

Previously drilled boreholes of a host rock for a potential nuclear waste repository in Hungary revealed a highly fractured claystone rock body. A crucial step for characterizing the hydrodynamic behavior of such a fractured reservoir is fracture identification and accurate calculation of the fracture density. Although acoustic borehole televiewers provide a reliable base for determining the fracture density, older boreholes usually lack such data. However, conventional borehole geophysical measurements are often accessible in such cases. The aim of this study was to identify any correlations between well log data and fracture density. Multiple linear regression analysis was performed on data from two boreholes penetrating the Boda Claystone Formation in southwest Hungary. The upper section of the BAF-4 borehole was used for training, where the fracture density was estimated with a fit of R² = 0.767. The computed regression function predicted the fracture density with high accuracy in both boreholes for all intervals with typical lithological features. However, in some sections where anomalous well log data indicated changes in the lithology, the prediction accuracy decreased. For example, the function underestimated the fracture density in sandy intervals.

El-Bakriya area is situated in the Central Eastern Desert of Egypt. It includes several mineral occurrences and/or deposits. Data from remote sensing and airborne geophysics (gamma-ray and magnetic) were combined to identify the hydrothermal alteration zones and structures related to these occurrences. Various processing of ASTER satellite images revealed fifteen zones of high probability for metallic mineralizations. Airborne gamma-ray spectrometric data clarify K-enrichment zones, which are correlated geologically with the distribution of calc-alkaline, alkali feldspar granites (Younger granites), and calc-alkaline quartzdiorites to granodiorites (Older granites), as well as basement metavolcanics and metasediments. An integrated hydrothermal alteration map was constructed, based on both ASTER remote sensing and airborne gamma-ray spectrometric data. This map delineates some metallic mineral occurrences and/or deposits, which are located in the detected hydrothermal alteration zones. Besides, it displays new potential zones for mineralization, such as: Gabal (G.) El-Shalul, G. El-Bakriya, G. Siwat El-Arsha and G. Umm Bisilla. The geologic and airborne magnetic maps demonstrate five structural lineament trends of E–W, ENE–WSW, NE–SW, NW–SE, and NNW–SSE directions, which might act as pathways to transport the hydrothermal solutions in the study area.

This work presents a technique to transform a global spherical to an adjusted local rectangular harmonic model. First, the mathematical form of a global spherical harmonic model is presented. Second, the necessary conversion from global (geocentric) into local rectangular coordinates is given. Third, Laplace’s equation is solved by the method of separation of variables in local rectangular coordinates and its solutions in different functional forms are presented. Then, the estimation of the coefficients of these mathematical models by a least squares’ adjustment process is described, using as data the values of the disturbing potential of the Earth’s gravity field. The strategy for the selection of the best mathematical model for a successful transformation is described and validated in different case studies. These refer to areas in Greece, China and Germany and include comparisons with other models or methods. The results show the applicability of the presented transformation and confirm its advantages.

The well-known, traditional way to extend P-wave acoustic impedance data between and beyond the well log locations is the post-stack inversion of seismic data usually available in the surroundings of the boreholes. A relatively new trend in the seismic exploration is based on the pre-stack inversion of the seismic CDP gathers providing both the P- and S-wave acoustic impedance sections (and volumes), as well as the estimated density data. This methodology is often called as simultaneous model-based inversion and can be utilized not only for hydrocarbon exploration, but it might also be a useful tool for the investigation of geothermal resources. In this study, we will compare the results of post-stack and pre-stack acoustic impedance inversions utilizing the same seismic volume. We will demonstrate and analyze the inverted attribute sections (and some of their derivatives) obtained by the pre-stack algorithm in detail. Finally, we will draw the conclusions about the lithological discrimination of the studied complex carbonate geothermal reservoir located in the pre-Cenozoic basement of the Little Hungarian Plain.

Road structure is composed of different pavement materials. These materials contain a large number of particles and pores with different sizes, shapes, dielectric properties and spatial locations, which determines the electromagnetic properties of roads. These feature multi-scale and discontinuous characteristics between layers, together with geometric irregularity and random non-uniformity characteristics within layers, therefore random structures will undoubtedly have a negative impact on GPR detection and data interpretation. As a supplement to the experimental observation, the forward modeling based on random media model can provide an economical and effective way for GPR detection of road hidden diseases. In this paper, discrete random media model and continuous random media model are established respectively by using digital image processing technology and stochastic process theory according to the structural characteristics of different layers of media in road structure. On the basis of the established random media model, the GPR response of holes and pipelines are simulated and analyzed by the Finite Difference Time Domain method, and the GPR signal is processed by the synthetic aperture focused imaging method. By comparing with homogeneous layered models, the results show that the forward modeling based on random media model can reflect the characteristics of ground penetrating radar signal of road structure more accurately. PVC pipe is accompanied by obvious multiple waves in the case of water filling. The polarity relationship between reflected wave and direct wave is the key to distinguish whether it is iron pipe or cavity. Synthetic aperture focused imaging algorithm can enhance the recognition of spatial location, size and dielectric properties of the target. The comparison with the field results shows that the simulated results are in reasonable agreement with the measured ones.

The seismicity of Romania is mostly represented by earthquakes produced by the Vrancea seismic source with intermediate depth events (3 shocks/century with magnitude M_W greater than 7.0). The seismic activity in Romania also includes crustal earthquakes. The crustal seismicity is more scattered and moderate compared to the intermediate-depth one. A stable and automatic method has been implemented in the real-time data acquisition and processing system ANTELOPE to estimate the seismic moment, the moment magnitude and the corner frequency of events recorded by the velocity sensors, using spectral analysis applied to S waves. The main goals are the independent estimation of the seismic moment and the common characterization for all events recorded by the National Seismic Network. The main target of this paper is represented by the fast estimation of moment magnitude M_W and ground motion parameters that are derived using Gallo et al. (Bull Earthquake Eng 12:185–202, 2014) methodology and their validation with other magnitude determination algorithms existing at the National Institute for Earth Physics (NIEP). To test this new methodology, we have analyzed 331 seismic events, most of them being automaticaly located, and afterwards added a new, manually processed solution for events with M_L ≥ 4.5, to obtain a larger interval of magnitudes.

Salt crystallization is the most important weathering processes that causes problems for rocks used as building stones. Therefore, assessment of the physico-mechanical properties of stones against cyclic salt crystallization is a critical issue for rock engineering applications in salty environmental conditions especially prone to abrasion. This research aims at investigating the relationship between the degradation of various physico-mechanical properties including apparent porosity, point load index, unconfined compression strength and abrasion loss rate for rocks during salt weathering process. For showing rock weathering in the salt crystallization process, five kinds of carbonate rocks were sampled from different areas of Egypt and subject to cyclic salt crystallization, up to twenty cycles. The variation rates of their physico-mechanical characteristics and abrasion loss values were calculated after each five cycles of salt weathering. The results showed that the studied rocks are suitable for use as building and decorative stones in areas prone to crystallization of salts, but attention must be paid to rocks with high porosity and low strength characteristics such as El-Minia limestone specimens. It was also found that there are strong linear correlations between the physico-mechanical degradation parameters and the abrasion loss rate of the studied rock samples at the end of the twentieth cycle of salt weathering. These relationships may be used to estimate the abrasion loss rate of the studied rock types against the cyclic salt weathering conditions and hence to make a rapid evaluation of the stone durability.

The Landsat program, which started in 1972 with Landsat-1, continues today with its newest satellite, Landsat-9, launched on 27 October 2021. The Landsat-9 data have been freely distributed since 10 February 2022 on the Earth Explorer platform. However, no scientific study on Landsat-9 for land use/land cover (LULC) mapping has yet been published, focusing on specific eco-systems. Therefore, the present study investigates the potential of Landsat-9 images for LULC classification in forest and agricultural systems. To achieve this, we selected two study areas, i.e. Kaynarca (forest-dominated) and Hocalar (agriculture-dominated), from different ecoregions of Turkey. Then, we mapped their LULCs using Landsat-8 and Landsat-9 data with the Support Vector Machine, K-Nearest Neighbors (K-NN), Light Gradient Boosting Machine (LightGBM), and 3D Convolutional Neural Network (3D-CNN) methods. The classification accuracies were assessed with the F1-score, taking the stand-types maps of the case areas as reference. It was seen that the best maps were generated by the 3D-CNN method with accuracy rates of 88.0% for Kaynarca (Landsat-8) and 87.4% for Hocalar (Landsat-9) at the landscape level. Unlike other methods, 3D-CNN removed the “salt-and-pepper effect” on the maps providing better spatial structure for further analyses. Regardless of the satellite missions, the mapping accuracies for the “productive forest” and “agriculture” classes were > 90% for Kaynarca and Hocalar, respectively. The comparative results suggest that Landsat-9 offers satisfactory LULC maps with similar classification accuracies as Landsat-8 and can be effectively used as a freely available remote sensing resource in monitoring and mapping forest- and agriculture-dominated landscapes.

This paper presents a method for parameter estimation of self-potential (SP) anomalies using neural networks. The General Regression Neural Network (GRNN) one-pass learning algorithm was performed to invert SP anomalies of simple shaped geometrical bodies approximation. The one-pass learning algorithm has a certain advantage in terms of computation time compared to classical neural networks because the classical neural networks use multiple learning steps. The presented algorithm was tested on noise-free and noise-corrupted synthetic data. In addition, the method was applied to three field examples: Süleymanköy, Weiss, and Sarıyer anomalies, respectively. The model parameters including electric dipole moment, polarization angle, depth, shape factor, distance from the origin of the anomaly, base slope and the base level were successfully estimated using the presented method. The frequency distribution of each model parameter was calculated to improve and overcome the ambiguity of the estimated model parameters. To investigate the correctness of the estimated model parameters, the obtained results were compared with previous studies. Thus, the agreement between the results obtained by the present method and other previous results is similar to most of the estimated model parameters in accordance with numerical values. The result of the present study shows that the GRNN can be used as a powerful parameter estimation tool in the interpretation of SP data in terms of computation time compared to artificial neural networks.